Arm-Helium-Technology
A reference book on M-Profile Vector Extensions (MVE) for Arm Cortex-M Processors (by arm-university)
microflow-rs
A Rust TinyML compiler for neural network inference on embedded systems (by matteocarnelos)
Arm-Helium-Technology | microflow-rs | |
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1 | 1 | |
6 | 59 | |
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5.7 | 7.8 | |
11 months ago | 3 months ago | |
Rust | ||
GNU General Public License v3.0 or later | Apache License 2.0 |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Arm-Helium-Technology
Posts with mentions or reviews of Arm-Helium-Technology.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-16.
microflow-rs
Posts with mentions or reviews of microflow-rs.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-16.
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TinyML: Ultra-low power Machine Learning
I built a Rust TinyML compiler for my master thesis project: https://github.com/matteocarnelos/microflow-rs
It uses Rust procedural macros to evaluate the model at compile time and create a predict() function that performs inference on the given model. By doing so, I was able to strip down the binary way more than TensorFlow Lite for Microcontrollers and other engines. I even managed to run a speech command recognizer (TinyConv) on an 8-bit ATmega328 (Arduino Uno).